This application is related to co-pending commonly assigned U.S. patent application entitled xe2x80x9cSystem and Method For Adapting an Equalizer in the Presence of Non-Stationary Noisexe2x80x9d filed on even date herewith, which is incorporated herein by reference.
The present invention is generally related to determination of data transmission capability parameters of communications systems employed in a network, and more particularly to a system and method for estimating signaling error probability in a communications link by determining the power levels of noise.
With the increasing bandwidth demands from the advent of the Internet, service providers have looked for ways to increase data transmission performance over the copper wire local loop transmission lines that connect the telephone central offices (COs) to the customer premises (CPs). The customer premises equipment (CPE) is connected to the CO switches over the above mentioned transmission lines known as xe2x80x9clocal loops,xe2x80x9d xe2x80x9csubscriber loops,xe2x80x9d xe2x80x9cloops,xe2x80x9d or the xe2x80x9clast milexe2x80x9d of the telephone network. Historically, the public switched telephone network (PSTN) evolved with subscriber loops connected to a telephone network with circuit-switched capabilities that were designed to carry analog voice communications. Digital service provision to the customer premises is a more recent development. With it, the telephone network has evolved from a system capable of only carrying analog voice communications into a system which can simultaneously carry voice and digital data.
Because of the prohibitive costs of replacing or supplementing existing subscriber loops, technologies have been implemented that utilize existing subscriber loops to provide easy and low cost migration to digital technologies. Subscriber loops capable of carrying digital signals are known as digital subscriber lines (DSLs). Logical channels within a subscriber line which carry digital signals are known as DSL channels, while logical channels within a subscriber line which carry plain old telephone service (POTS) analog signals are known as POTS channels. Some DSL technologies, such as but not limited to integrated services digital network (ISDN), high-bit-rate digital subscriber line (HDSL), HDSL2 and symmetric digital subscriber line (SDSL), may utilize portions of the POTS channel and therefore do not coexist with a POTS signal. Other digital technologies provide customers with additional flexibility and enhanced services by utilizing frequency-division multiplexing and/or time-division multiplexing techniques to fully exploit a subscriber loop with multiple logical channels. These newer multiple channel DSL technologies provide digital service to the customer premises without significantly interfering with the existing POTS equipment and wiring. The newer DSL technologies accomplish this functionality by frequency-division multiplexing (FDM) their digital signal above (at higher frequencies than) the 0 KHz to 4 KHz frequency range, within which standard analog POTS signals are carried. Multiplexing techniques and terminology are common to those skilled in the art, and are not described herein.
Several variations of new multiple channel DSL technology exist, such as but not limited to Asymmetric Digital Subscriber Line (ADSL), Rate Adaptive Digital Subscriber Line (RADSL), Very High Speed DSL (VDSL), Multiple Virtual Lines (MVL(trademark)) and Tripleplay(trademark), with this group generally referred to as xDSL. Communications systems carrying xDSL may multiplex xDSL signals and a POTS signal onto a single physical local loop.
Historically, the POTS subscriber loop was designed with the functions needed to communicate both analog, voice-conversation signals and subscriber loop signaling. The CO switch uses subscriber loop signaling to notify the customer premises about events in the telephone network, while customer premises equipment (CPE) use subscriber loop signaling to inform the CO to perform actions for the customer. Some examples of subscriber loop signaling include: the CO switch signaling to the CPE that an incoming call has arrived by ringing the phone, the CPE (e.g., a telephone) signaling to the CO switch that the CPE is initiating a call by an on-hook to off-hook transition of the telephone handset, and the CPE signaling to the CO switch that a call should be connected to a location by sending the phone number of the location.
Although the transmission of both digital signals and analog POTS signals over a subscriber loop offers many potential advantages for customers, several practical problems must be solved when implementing DSL solutions. One significant problem resulting from the POTS subscriber loop signaling functions is the generation of high-frequency interference or noise into DSL channels. This high-frequency noise interferes with the decoding of a received signal. One category of noise is predictable to a reasonable degree. This predictable noise is often referred to as stationary (or cyclo-stationary) noise. Noise that is stationary or slowly varying can be anticipated, and to a degree corrected for, in the transmission of a digital signal.
Another category of noise is commonly referred to in the art as non-stationary noise. Non-stationary refers to the statistically unpredictable nature of the noise over the time period of interest. That is, it is more difficult to anticipate when non-stationary noise will occur, anticipate the strength of the non-stationary noise, or anticipate the duration of the non-stationary noise. For instance, a telephony system on-hook/off-hook signal or a pulse-dialing signal are square waveforns which have high-frequency components and harmonics. Theoretically, these telephony system signals require infinite frequency bandwidth and are therefore difficult to anticipate and compensate.
Another source of noise is crosstalk. Crosstalk is undesirable interference or noise that is induced into a channel by signals travelling in adjacent subscriber loops sharing the same underground cable or overhead wire. FIG. 1 is a schematic view of a prior art communication system showing a CO 22 connected to a CP 24 via a single subscriber loop 26. Typically, many individual subscriber loops 28 are bundled together at convenient locations into one cable 30. The cable 30 extends back to the CO 22. The close proximity of the many subscriber loops 28 to subscriber loop 26 results in magnetic and/or capacitive coupling between subscriber loop 26 and some of the other subscriber loops 28 adjacent thereto. Undesirable interference may be induced into subscriber loop 26 as various communication signals are transmitted across the subscriber loops 28. For example, one type of crosstalk occurs when a modem rapidly and repeatedly transitions between an ON state (transmitting data) and OFF state (not transmitting). During the ON state, the modem induces noise onto adjacent subscriber loop 26 which has characteristics similar to stationary noise. However, when the modem in the OFF state, no noise is induced into subscriber loop 26. This noise, induced into subscriber loop 26 by a modem which is rapidly transitioning between the ON and OFF states, has characteristics of both stationary and non-stationary noise, and is referred to in the art as short-term stationary (STS) noise. Since an individual cable may contain up to several thousand subscriber loops, STS noise can be a commonly encountered noise source.
FIG. 2 shows a typical data constellation as would be used in carrierless amplitude/phase modulation (CAP), quadrature amplitude modulation (QAM), Discrete MultiTone (DMT), or similar DSL modulation techniques. In this illustrative example, a 16 point constellation 40 is shown as a series of points 410 through 425 aligned with X axis 42 and Y axis 44 on a two dimensional grid. Each point represents a symbol which may be sent from the DSL transmitter to the DSL receiver. The symbols may be corrupted during transmission by channel distortion, noise, crosstalk, and the like. The receiver will determine which symbol was transmitted by the remote transmitter by determining in which decision region 46 the received symbol (after any equalization and/or other processing) lies, as defined by decision boundaries 48. Note that X axis 42 and Y axis 44 are also decision boundaries in this illustrative example.
As a simplified hypothetical example, consider the case of two symbols received at different times. In the first case, the remote transmitter sent symbol 412. During transmission, the symbol was corrupted by error 50 and after receiver processing was detected at position 412xe2x80x2. Since position 412xe2x80x2 is within the decision region 46 identified with symbol 412, the receiver correctly determined that symbol 412 had been sent and the symbol was decoded correctly.
In the second case, the remote transmitter sent symbol 414. During transmission, the symbol was corrupted by error 52. After receiver processing, the symbol was detected at position 414xe2x80x2. Since position 414xe2x80x2 is within the decision region 46 identified with symbol 415, the receiver incorrectly determined that symbol 415 had been sent and the symbol was decoded in error.
The primary difference between the first and second cases described above is the magnitude of the error that displaced the received symbol from the position at which it had been transmitted. In the first case, the magnitude of the error 50 was small enough that the received symbol 412xe2x80x2 correctly remained in the same decision region 46 as symbol 412. In the second case, the magnitude of the error 52 was sufficiently large to move the received symbol 414xe2x80x2 into the adjacent decision region 46 of symbol 415, thereby resulting in the decoding error. The threshold magnitude at which a noise sample can potentially cause a decision error is shown in FIG. 2 as dimension 54, the distance between the transmitted point at the center of the decision region 46 and the decision boundaries 48.
Data transmission rates, transmission signal strength, constellation densities, and other parameters can be optimized to condition a transmitted signal such that the received signal is within acceptable parameters even in the presence of many types of noise. The selection of signal transmission parameters is based upon estimation of the stationary noise, and/or non-stationary noise. However, this approach is effective only when accurate estimations can be made. If the estimation is overly conservative, data may be transmitted at rates which are less than the theoretical maximum. If the estimation is overly optimistic, the transmitted data may be subject to errors resulting from the interfering noise.
A basic problem for designers of a data communication system is the estimation of error statistics associated with a received signal. If actual noise in the communication system exceeds the noise level anticipated by the designer, the probability that decoding errors will exceed the desired design probability for the symbol error rate, typically 10xe2x88x926 or below (one error in 106 data symbols), will be high. Error estimation, also known as noise estimation hereinafter, can be used to identify potential problems, such as noise sources in a communication system, or to determine the appropriate transmission rates of data in a rate adaptive system. Examples of rate adaptive systems include ADSL (Asymmetric Digital Subscriber Line), RADSL (Rate Adaptive Digital Subscriber Line), MVL (Multiple Virtual Line), Tripleplay(trademark), or the like.
FIG. 3 is a block diagram illustrating one implementation of a prior art receiver 62. The receiver 62 could be implemented as hardware in a chip set, as firmware on a general purpose digital signal processor (DSP), as software residing in memory associated with a central processing unit (CPU), or the like. The receiver 62 can be implemented in a variety of digital communication devices, such as but not limited to, an external modem, a personal computer (PC), a PC modem card, a line card or the like. One skilled in the art will realize that a receiver 62 can be implemented in a variety of manners as is a common practice in the art, and that the prior art receiver 62 of FIG. 3 is provided as an illustrative example.
Describing now in greater detail FIG. 3, an input signal 64 arrives at the receiver 62. After processing the input signal 64, an output signal 66 is transmitted from the receiver 62 to digital equipment (not shown) residing at the CO 22 or the CP 24 (FIG. 1). In addition, an estimate of the received noise 68 is transmitted from receiver 62 to digital equipment (not shown) residing at the CO 22 or CP 24 (FIG. 1) to facilitate rate adaptation, error reporting, or the like. The receiver 62 has a decoder 70, a demapper 72, and an RMS noise estimator 74. Decoder 70 is connected to demapper 72 by line 76 and to RMS noise estimator 74 by line 78. Demapper 72 provides the output signal 66 via line 80. RMS noise estimator 74 provides the noise estimator output signal 68 via line 82. Two output signals are generated by decoder 70. Sliced symbol output signal 84 contains decoded symbols such as symbol 412 (FIG. 2) which are then further processed by demapper 72. Error signal 86 contains error samples such as error 50 and error 52 (FIG. 2) and is used by RMS noise estimator 74 to generate an estimate of the noise. The RMS noise estimator 74 processes the error signal and provides a RMS noise estimator output 68 via line 82. RMS noise estimator output 68 may then be processed in any conventional manner commonly employed in the art. For example, the RMS noise estimator output 68 could be transmitted to a database system for storage and later retrieval. The RMS noise estimator output 68 could be processed by software designed to generate a noise estimator output report (not shown) and which may be displayed in any conventional manner, such as but not limited to, a graphics device, a line printer, an X-Y plotter or the like. Noise estimator output 82 may also be used as an input to a rate adaptation function which may result in an increase or decrease in the rate at which data is sent to receiver 62.
Noise estimation for stationary noise is usually accomplished by measuring the power or the root mean squared (RMS) magnitude of the noise. The power or the magnitude of the noise is then used to estimate the probability of symbol error rates. Noise can be probabilistically described in terms of standard deviation and per unit power, as shown by the graph 90 of FIG. 4. Stationary noise is generally bounded with a classical Gausian distribution, as shown by the dashed line 94. However, the long term average distribution of noise having STS noise components may not be not bounded by a Gausian distribution, as shown by the solid line 96.
FIG. 5 is a graph 102 illustrating the difficulty of estimating noise with prior art methods when STS noise is present with stationary noise. The graph 102 shows a simplified illustrative example sample period with N samples. A first portion 104 is shown, at samples 0-2, where only stationary noise is present. No STS noise is present during this first portion 104. The power P1 of the error signal, consisting of stationary noise only, is shown as being constant.
A second portion 106 is shown where STS noise is present. In this simplified illustrative example at sample M+1, a first stationary noise (STS 1) comes on (a first modem, not shown, is transmitting and inducing a noise signal into the system), which raises the power of the error signal to P2. Then, a second STS noise (STS 2) comes on at M+2 (a second modem is transmitting and inducing a noise signal into the system) raising the power of the error signal to P3. Similarly, STS 3 and STS 4 come on at M+3, further raising the power of the error signal to P5. As the STS noises come on, the power of the error signal increases in this illustrative example. Then, STS 1 is shown to come off at M+7, reducing the power of the error signal to P4. At M+8, STS 2 and STS 3 come off, leaving only STS 4 on. The power of the error signal is then P2. At M+9, all STS noise sources are off, and the power of the error signal is P1 and consists only of stationary noise. In this simplified illustrative example, all STS noise sources remain off through the end of the sample period N, as shown by the portion 108. One skilled in the art will realize that the simplified illustrative example shown in FIG. 5 is equally applicable to other types of non-stationary noise.
With the prior art, data transmission rates might be determined based upon a design power of the error signal having only the stationary noise (no STS noise sources or other non-stationary noise sources present). When modems communicating over adjacent wire pairs begin transmitting and induce noise onto the communication system, the power of the error signal increases. With a plurality of STS noise sources, the actual power of the error signal may significantly exceed the above-described design power of the error signal (the assumed error signal power used to determine data transmission rates). When this situation occurs, the probability of signal errors increases, and performance degradation may occur.
FIG. 6 is a flow chart 112 illustrating one prior art noise estimation method. The method begins with initialization of a predefined sampling period of length N at block 114. The sample number K and the integrated power P are set to zero at block 116. Then, K is incremented at block 118 and the first new sample X is received at block 120. A running total of the integrated power P is calculated by squaring the magnitude of the sample (|X|*|X|) and adding it to the running total P at block 122. The sample number K is then checked to determine if the sampling period N has expired at block 124. If K is less than N, the No condition, then the process returns to block 118. If K is equal to N, the Yes condition, then the running total of the integrated power P is divided by N at block 126 to calculate the average mean power of all samples received during the sampling period N. The average mean power, P/N, is thus calculated and the process ends, as shown at block 128. One skilled in the art will realize that the square root of the calculated mean power is the root mean square (RMS), another valid indicator of noise on the communication circuit.
With this prior art method as described in FIG. 6, if STS or non-stationary noise is absent during all or a portion of the sampling period, data transmission rates will be determined without consideration of the possible effect of STS or non-stationary noise sources. If STS or non-stationary noise occurs on the communication circuit when a modem is receiving data, the probability of decoding errors increases.
Thus, a heretofore unaddressed need exists in the industry for a way to more accurately account for the effect of STS and/or non-stationary noise sources when estimating noise characteristics in a communication system.
The present invention provides a thresholding noise estimator that resides in a receiver which detects and samples an error signal during a sampling period. In a preferred embodiment when the magnitude of a data sample from the error signal, also known as an error sample, is at least equal to a predefined threshold, that sample is included in the noise estimation calculation. At the end of the sampling period, the computed power for each of the included error samples are averaged. This average power corresponds to an error indicator which quantitatively indicates the power for that portion(s) of an error signal which is at least equal to the threshold. That is, the power of each data sample having a magnitude at least equal to the predefined threshold (i.e., an error sample) is computed, and at the end of the sampling period, the calculated powers are averaged.
An alternative embodiment of the thresholding noise estimator provides for magnitude hysteresis. Similar to the preferred embodiment, the power is calculated for an error sample having a magnitude at least equal to a first predefined threshold. Power for succeeding error samples having a magnitude at least equal to the threshold is calculated. However, once the power calculating process begins, the power of succeeding data samples are calculated until the magnitude of a succeeding data sample drops below a second predefined threshold. Once the magnitude of this succeeding data sample drops below the second predefined threshold, the power calculations are halted. That is, succeeding data samples are defined as error samples until the magnitude of the error signal drops below the second threshold. (Power calculations do not begin again until the magnitude of the data samples again exceeds the first threshold.) At the end of the sampling period, the computed powers are averaged. This average power corresponds to an error indicator which quantitatively indicates the power for the above-described portion(s) of an error signal.
Another alternative embodiment of the thresholding noise estimator provides for time hysteresis. Similar to the preferred embodiment, the power is calculated for an error sample having a magnitude at least equal to a first predefined threshold. Power for succeeding error samples having a magnitude at least equal to the threshold is calculated. However, when the first succeeding data sample having a magnitude below the threshold is detected, the power of succeeding data samples are calculated until the expiration of a predetermined time period (or alternatively, after the detection of a predetermined number of succeeding data samples). Then, the power calculations are halted. That is, succeeding data sample s are defined as error samples until the magnitude of the error signal drops below the threshold and after the expiration of the time period. (Power calculations do not begin again until the magnitude of the data samples again exceed the first threshold.) At the end of the sampling period, the computed powers are averaged. This average power corresponds to an error indicator which quantitatively indicates the power for the above-described portion(s) of an error signal. Alternatively, an embodiment employing time hysteresis may use a predefined number of data samples instead of a predefined time period either being functionally equivalent.
Another alternative embodiment of the thresholding noise estimator provides a combination of magnitude and time hysteresis. The thresholding noise estimator begins calculating the power of error samples upon the detection of a data sample having a magnitude which is at least equal to a predefined first threshold. Power calculations continue until the magnitude of succeeding error samples decrease to less than a predetermined second threshold (magnitude hysteresis). Then, power calculations are further continued until the expiration of a predetermined time period, or, until the detection of a predetermined number of data samples (time hysteresis). At the end of the sampling period, the computed powers are averaged. This average power corresponds to an error indicator which quantitatively indicates the power for the above-described portion(s) of an error signal.
Yet another alternative embodiment of the thresholding noise estimator adds to the scaled averaged power the scaled power of that error sample having the greatest magnitude of the error samples detected during the sampling period (i.e., the peak error sample). The average power for this alternative embodiment may be calculated by any one of the above-mentioned embodiments. This scaled average power plus the scaled power of the peak error sample corresponds to an error indicator which quantitatively estimates the maximum peak power for an error signal which will occur with a given probability.
And another alternative embodiment interleaves the error samples into a predetermined number of sample subsets. Then, the magnitude of the peak error samples from each sample subset are averaged. This averaged peak magnitude is scaled and added to the scaled average power calculated by any one of the above-mentioned embodiments. This scaled average power plus the scaled averaged peak magnitude corresponds to an error indicator which quantitatively estimates the maximum peak power for an error signal which will occur with a given probability.
The thresholding noise estimator may be implemented in a variety of other formats, including program code residing on a computer readable medium.
Other systems, methods, features, and advantages of the thresholding noise estimator will be or become apparent to one with skill in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the thresholding noise estimator, and be protected by the accompanying claims.